Assessing Symptoms And Concerns Characteristic Of Eating Disorders Among Non-elite Multisport Endurance Athletes
Bibliographic record
Abstract
Stringent management of body weight is considered by many athletes to be an intrinsic component of performance. Weight management is of particular importance in elite athletes involved in multisport endurance events. However, the prevalence and magnitude of concerns about food and body weight in non-elite multisport endurance athletes is unknown. PURPOSE: To evaluate symptoms and concerns related to eating disorders among non-elite athletes involved in multisport endurance summer and winter events. METHODS: A total of 145 non-elite athletes (102 men and 43 women) were recruited from the following multisport endurance events: winter triathlon, winter pentathlon, half-Ironman and Ironman. Self-reported symptoms and concerns related to eating disorders were assessed using the validated Eating Attitude Test-26 (EAT-26) questionnaire. RESULTS: Mean age (±SD) of participants was 39.6±10.8 years while age and gender-specific ranking in the sporting events was 49.0±27.4%. The mean EAT-26 score (±SD) was 7.1±6.5 with higher values among women than men (9.4±7.9 vs. 6.2±5.5, P=0.004). The EAT-26 scores were also higher among athletes involved in the half-Ironman than among athletes competing in winter multisport events (10.1±1.1 vs. 6.0±1.0, P=0.04). When adjusted for sex, there was no correlation between percentile ranking in the sporting event and the EAT-26 score. Finally, only 8 athletes (5.5 %) scored above the EAT-26 cut-off score of 20 for eating disorders. CONCLUSIONS: Unsurprisingly, non-elite multisport endurance female athletes scored higher on the EAT-26 than non-elite male athletes. However, the prevalence of self-reported symptoms and concerns about food and body image was very low in this sample of non-elite endurance athletes. This suggests that multisport endurance environments do not significantly induce and sustain unhealthy behaviors towards foods and body image.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".